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2 This report has been commissioned by: The Royal Automobile Club Foundation for Motoring Ltd is a transport policy and research organisation which explores the economic, mobility, safety and environmental issues relating to roads and their users. The Foundation publishes independent and authoritative research with which it promotes informed debate and advocates policy in the interest of the responsible motorist. The Office of Rail Regulation (ORR) is the independent economic and safety regulator for Britain s railways. We regulate health and safety standards and compliance across the industry and we set Network Rail s funding and outcomes. We are also responsible for competition and consumer rights issues, economic and safety regulation of HS1 and the publication of key statistics on railway performance. We work with the industry s funders in England, Scotland and Wales to get clarity on what they want the railways to deliver for the 3.9 billion a year they spend on rail. The Independent Transport Commission is one of Britain s leading research charities with a mission to explore all aspects of transport and land use policy. Through our independent research work and educational events we aim to improve and better inform public policy making. For more information on our current research and activities please see our website. Transport Scotland is the Scottish Government s national transport agency responsible for; aviation, bus, freight and taxi policy; coordinating the National Transport Strategy for Scotland; ferries, ports and harbours; impartial travel services; liaising with regional transport partnerships, including monitoring of funding; local roads policy; major public transport projects; national concessionary travel schemes; rail and trunk road networks; sustainable transport, road safety and accessibility; the Blue Badge Scheme. Transport Scotland is an Executive agency accountable to Scottish Ministers. Published by: RAC Foundation Pall Mall London SW1Y 5HS Tel no: Registered Charity No December 2012 Copyright Royal Automobile Club Foundation for Motoring Ltd

3 Rail Demand Forecasting Using the Passenger Demand Forecasting Handbook On the Move Supporting Paper 2 Tom Worsley, University of Leeds December 2012

4 This Study The main findings of the study are reported in On the Move: Making sense of car and train travel trends in Britain. A series of technical reports describe aspects of the work in more detail, and are available on the sponsors websites: A supporting technical compendium containing figures and tables that were prepared but have not been included in this summary report Rail Demand Forecasting Using the Passenger Demand Forecasting Handbook National Rail Passenger Survey Data Analysis A report on trends in Scotland, using both NTS data and data from the Scottish Household Travel Survey Acknowledgements The authors are grateful to the Steering Committee for their suggestions throughout this study, and for the advice and assistance of other members of the study team. Particular thanks are due to Luca Lytton for his efforts in the design and preparation of this report. The members of the Steering Committee were: David Bayliss, RAC Foundation Stephen Glaister, RAC Foundation David Quarmby, RAC Foundation Luca Lytton, RAC Foundation Ivo Wengraf, RAC Foundation Nicholas Finney, Independent Transport Commission Simon Linnett, Independent Transport Commission Matthew Niblett, Independent Transport Commission Emily Bulman, Office of Rail Regulation Rachel Hayward, Office of Rail Regulation Deren Olgun, Office of Rail Regulation Kathy Johnston, Transport Scotland Charles Buckingham, Transport for London Simon Nielsen, Transport for London Taro Hallworth, Department for Transport Paul O Sullivan, Department for Transport Peter Headicar, Oxford Brookes University Stephen Joseph, Campaign for Better Transport Professor Peter Mackie, Institute for Transport Studies, University of Leeds Kit Mitchell, Independent Transport Consultant Disclaimer This report has been prepared for the RAC Foundation, Office of Rail Regulation, Independent Transport Commission and Transport Scotland by Tom Worsley. Any errors or omissions are the author s responsibility. The report content reflects the views of the author and not necessarily those of the four sponsors. The author and publishers give permission for the charts, analyses, text and other material contained in this report to be reprinted freely for noncommercial purposes, provided appropriate reference is made. i

5 About the Study Team Professor Peter Jones is Professor of Transport and Sustainable Development in the Centre for Transport Studies at University College London, and has been the Project Director for this study; he was an author of The Car in British Society report, published by the RAC Foundation in 2009, which initially drew attention to the levelling off in car use nationally. He has carried out many studies, both in the UK and internationally, into travel patterns, public attitudes and factors affecting travel behaviour. He is a Member of the Independent Transport Commission. Charilaos Latinopoulos is a Research Assistant in the Centre for Transport Studies, Imperial College London. He is currently performing a doctorate addressing questions surrounding consumer demand for electric vehicles, and previously worked in the private sector as a transportation consultant. Dr Scott Le Vine is a Research Associate in the Centre for Transport Studies, Imperial College London. He serves on the Transportation Research Board s standing committee on Public Transport Innovations, and is a trustee of the charity Carplus. His recent study Car Rental 2.0 is available on the RAC Foundation website. Professor John Polak is the Chairman of the Centre for Transport Studies and the Director of Research in the Department of Civil and Environmental Engineering, both at Imperial College London. He is a past President of the International Association for Travel Behaviour Research and a past Council Member of the Association for European Transport, and serves on the editorial advisory boards of a number of leading international scientific journals. Fiona Preston is a Research Assistant in sustainability in the Centre for Transport Studies at University College London. She works on sustainable transport and development issues including rail travel growth, transport geography and transition towns. Previous positions include energy policy research at the University of Oxford and sustainable transport campaigning at Transport & Environment in Brussels. Tom Worsley is a Visiting Fellow in Transport Policy at the Institute for Transport Studies at the University of Leeds. His career prior to this was as an economist in the public sector, spending most of his time in the Department for Transport where he held a number of senior posts and was responsible for developing the Department s forecasting techniques. These included the rail based Network Modelling Framework and the National Transport Model, both of which are used to inform policymakers about prospects for road and rail traffic and options for managing demand or increasing capacity. ii

6 Contents 1. Introduction 1 2. Principles of Transport Modelling and Forecasting 3 3. Outline of PDFH and the Associated Rail Forecasting Method The Rail Forecasting Database LENNON The Passenger Demand Forecasting Handbook EDGE Exogenous Demand Growth Estimation MOIRA Specification of rail services and capacity The geographical coverage of the rail forecasts 9 4. PDFH Estimation Methods GDP elasticities Fares elasticities Employment and population elasticities Effects of other modes Changes in journey quality generalised journey time Critical Assessment of PDFH The lack of data on the characteristics of the passenger or trip in LENNON Elasticity values The choice of the base year Segmentation by ticket type and flow group Changes in technology How Successfully Does PDFH Represent Recent Trends in Rail Patronage? Options for Further Analysis Options for a behavioural rail model Improvements to PDFH Other models of aggregate rail demand Creating an opportunity for change References 31 iii

7 1. Introduction The main report On the Move on the factors that have contributed to car traffic and rail patronage has identified some changes in people s behaviour which are likely to have been influenced by the opportunities they are faced with when making a trip. For example, we have noted an increase in the proportion of the population that travels by train. Transport models are a way of identifying the key factors that influence the choices people make, and estimating the strength of those influences. By combining projections of how these influences might change in the future with forecasts of the size of the population, its composition and location, a transport model provides forecasts of the trips people are expected to make, the modes they will use, the places they will go to, and the routes they are expected to take. Section 1 of this report outlines the broad principles of the conventional transport model. Although the rail industry provides a comprehensive database of every ticket sold, which provides the station of origin and station of destination of the ticket, data on the characteristics of rail users is not generally available at the level of spatial detail that meets the rail industry s forecasting requirements. In place of a model that reflects the behaviour of individual households, rail models are based on the relationship between changes each year in the volume of passengers travelling by rail between a sample of the stations, and changes in the factors which econometric methods identify as the major drivers of rail demand. These drivers include changes in rail fares, in levels of employment in city centres, in GDP and in the quality of the rail service used. This report outlines, in Section 2, the Passenger Demand Forecasting Handbook (PDFH) and describes the rail forecasting framework as well as the set of models which provides for forecasts of flows, and which allocates these flows to the train services in the projected timetable. Section 3 provides a brief description of the methods used to estimate the values of the elasticities that link changes in the drivers of demand with the resulting growth (or reduction) in rail patronage, and of those used to provide for forecasts 1

8 of rail patronage which take account of changes to service quality, including the effects of crowding. One consequence of adopting a forecasting model based on ticket sales data and elasticity values is that it is not possible to assess the implications of our observations on the relevance of household and demographic factors, as recorded in On the Move for the forecasts of rail patronage. Section 4 then assesses some of the challenges that arise in deriving estimates of the values of the relevant elasticities. It is often the case that the effects of these different drivers of rail patronage cannot easily be identified separately; another difficulty is the attribution of cause and effect. Data on past changes in some of the drivers of demand is not always available at the level of detail required. And, since the method of forecasting rail demand provides for estimates of the growth from a specific base, the assessment of the forecasting methods identifies the requirement to select a base year which is representative, not one which has been affected by any unusual features. Without access to the models and database, it is not possible to provide a full assessment of the extent to which recent trends in rail demand can be explained by the changes in the factors that affect these trends, at a time when one of the main variables GDP has remained broadly unchanged. Section 5 provides a brief and very broad assessment, based on published data, of the factors that might have contributed to these trends, and the extent to which their contribution might explain the growth. The final section, 6, provides some suggestions for possible development of the rail modelling framework and for further work that might inform such a programme. 2

9 2. Principles of Transport Modelling and Forecasting 1. Transport forecasting and appraisal models are used to predict the demand to travel and, in many cases, to assess the impact of these forecasts on the level of service offered by the transport network. Such forecasting models are also used to estimate the impacts of changes in transport networks caused by investment in capacity or decisions about managing demand by means of pricing or other interventions. The form of model most widely used is referred to as a four-stage model, with the separate stages describing the processes of estimating the trips which are generated by the households in the study area, their choices of mode and of destination, and their assignment to a route. 2. The area covered by a transport forecasting model is determined by the interventions which the model is designed to address. Some models, such as the Department for Transport s (DfT s) National Transport Model (NTM), cover all modes and the entire country, and provide a platform for testing a range of policy options. Congestion on specific routes, or the transport problems of an urban area, are addressed through local models which contain a detailed representation of the available routes and options, and of the households and other places that account for the trips on the transport network of interest, but which provide no representation of travel outside the study area. 3. Most transport models include data on the population in the study area, its location, and its access to the transport network, using census and planning data, often supplemented by special surveys. The DfT s National Travel Survey (NTS) provides data on the number of trips made over a typical week by the households in the survey. The households in the transport model are broken down into several distinct household types, by number of household members and whether they include children, by employment status, and usually by whether or not they are car-owning. The categories are selected to distinguish between households according to the number of trips that they make, using evidence from local surveys or from the NTS, so as to take account of demographic change in the forecasts of travel demand. The transport model also includes data on the location of employment and of other economic activity that attracts the trips made by households. 3

10 4. The next phase links the trips which households produce with the locations that attract these trips. The choice of mode for each trip which, in strategic models, is generally between rail, bus and car depends for each household type on their level of car ownership; on the cost, trip duration and convenience of the modes available to them; and on the destination of the trip. The choice of destination is determined by a measure of the relative attractiveness of each area and also by the costs, duration and convenience of the trip. Trips are then assigned to the routes on the network that would provide transport users with their quickest and lowest-cost options as a means of taking account of the effects of congestion, which, by increasing the time taken for each trip affected, induces trips within the model to seek alternative routes. The costs of travel, as incorporated in the model, include both time costs including an estimate of the value of working time, and of time to people who are not travelling in the course of work and vehicle operating costs. 5. The estimates of the trips on the network derived from the transport model are then compared with such data as is available on actual traffic flows on the same network and, where available, records from household surveys on the trips that people make. Where, as is commonly (and unsurprisingly) the case, there are differences between the modelled flows and actual values, the former are adjusted to reflect actual volumes of traffic. The resulting modelled flows are called the base case. 6. The next phase of the forecasting process is to replace the data on the existing population by household type with projections of the population published by the Office for National Statistics (ONS), the details of which are agreed with local authorities by the DfT. The model is then used to estimate the trips that this future population would make on the assumption that households in each segment of the future population for example a car-owning two-adult pensioner household will make the same number of trips in the future as does the typical household of that segment in the 4

11 current population. Choice of mode, destination and route is determined by predictions from the model of the time and costs, including congestion, using the same techniques as were applied to estimate mode, destination and route choice in the base case. The model can then be used to assess the effects of changes in travel time and costs arising from changes in capacity or other interventions, and to estimate the cost and time savings that transport users would gain from such interventions. 7. There are many variations on and extensions of the classic four-stage transport model and its key elements as described above. Forecasts of changes in emissions and other pollutants are regularly derived from the estimates of changes in speed and in the fuel consumption element of vehicle operating costs. Some models focus on the peak period(s), since peak demand drives initiatives to invest in or otherwise manage capacity, whereas other models (e.g. those used to estimate CO 2 emissions) make separate forecasts of demand by journey purpose and time of day, sometimes allowing for changes in travel costs to influence people s choice of the time of day at which they travel. 8. The four-stage model has the merit that it sets out to represent transport user behaviour. Its accuracy can, to some extent, be challenged through an assessment of the responses estimated in the model. Moreover, if subsequent or otherwise better data suggests a change in responses, the model can be adapted to reflect this change. One of the factors prompting this research study was the recent evidence from the NTS showing reductions in average annual distance travelled by car by several segments of the population, raising questions about whether such trends, if indeed they proved to be more than a temporary effect, are adequately represented in the DfT s forecasting models. 9. One requirement of the four-stage model is an adequate coverage of the modes and trips of interest (for the purposes for which the model is being used) in the sample of households which forms the model s database. Both long-distance trips and trips by rail are made only by a small proportion of households in most areas of the country, and many such trips are made on an infrequent basis. Rail travel makes up around 2% of all trips, while accounting for 8% of all distance travelled. The NTS uses a spatially stratified sampling framework to identify households and the number of trips they make. However, although longer-distance trips make more use of the transport network in terms of miles travelled, the survey collects data based on the number of trips made. Since longer distance trips are made less frequently than shorter trips, relatively few of the longer distance trips are picked up in the survey despite the greater use that these trips make of the network in terms of mileage travelled. 1 1 This under-representation of long-distance trips has been recognised, and the DfT has enhanced the NTS so that it now includes details of longer-distance trips taken within two weeks of the seven-day period for which all other trips are recorded. 5

12 3. Outline of PDFH and the Associated Rail Forecasting Method 3.1 The Rail Forecasting Database LENNON 10. Because rail has a small share of the overall number of trips undertaken by the typical household, rail travel is rarely represented in any detail in the conventional four-stage model, other than in models covering London and other conurbations where rail has a larger market share. Even Transport for London s (TfL s) LATS (London Area Transportation Study) model takes forecasts from elsewhere (by using PDFH) for rail trips with origins or destinations outside the M Rail further differs from road travel in that there is a comprehensive database on the origins and destinations of the various separate rail stages involved in all train trips, because, where more than one operator s services are used in the course of a trip, all ticket sales are recorded for the purposes of accounting for the allocation of revenues to train operators and between train operators. The LENNON (Latest Earnings Networked Nationally Over Night) ticket sales database comprises a matrix covering the 2,500 or so stations on the network, containing a record of all tickets sold by ticket type (first, standard ordinary, standard reduced by type of discount: super saver, ordinary saver, season and so on). Data is provided for each of the 13 accounting periods in a year, and is available for each year since The data relates to ticket sales: LENNON does not contain any information on the train service used by the ticket holder or, in most cases, about the route taken. 6

13 12. Surveys of rail passengers provide information on the number of days on which season tickets of different periods of validity are used. Data from these surveys is used to estimate trips and distance travelled per season ticket. Surveys are also used to provide data on the origins and destinations and the frequency of use of Travelcards, and of other zonal tickets. The quality and coverage of these surveys differs between conurbations; neither Passenger Transport Executives (PTEs) nor train operators have strong incentives to collect good data on rail passengers use of Travelcards The Passenger Demand Forecasting Handbook 13. PDFH is a handbook which identifies all of the known drivers of rail demand and provides information on the values of the elasticities of these influences on demand. These elasticity values describe the percentage change in rail patronage that can be explained by a change in the demand driver for example, by a 1.5% increase in GDP per capita over the course of a year. In addition, PDFH provides its users with advice on how to apply the elasticity values to investigate changes in both the external environment (such as GDP, employment and fares) and changes in all of the attributes of a rail journey that influence its quality. PDFH is only one component of the rail forecasting framework. The elasticity values are combined with data on the flows on the route, or routes, from the LENNON database for which the forecasts are required, and with forecasts of the growth in the drivers of demand over the forecasting period. 14. Prior to privatisation, the analysis that supports PDFH was the responsibility of British Rail s Operations Research team and the Handbook was owned by British Rail. On privatisation, responsibility passed to the Association of Train Operating Companies (ATOC), who set up the Passenger Demand Forecasting Council (PDFC) to oversee and manage the development of PDFH and associated models, including the MOIRA (Model of Inter-Regional Activity) train service model. PDFC funds all research and development of this rail forecasting framework. A consequence of this arrangement is that PDFH is not a public document, since PDFC needs to restrict access to those who have contributed to its development, in order to reduce the opportunities for free-riding. For the purposes of this study, the researchers were given access to specific sections of the current version of PDFH. In order to maintain the confidentiality of PDFH, the elasticity values which have been quoted in this report are indicative of the broad magnitude or are restricted to those that have been published elsewhere, primarily in Revisiting the Elasticity Based Framework: Rail trends report (DfT, 2009). 2 Train operators, when bidding for a franchise, base their bid on the share of the Travelcard revenue they anticipate they will be allocated by the PTE, rather than an estimate of the passenger kilometres travelled by Travelcard holders and the average fare. Unlike bus services, train kilometres operated in the Travelcard area are not affected by the revenue allocated, and so PTEs have no incentive to increase the share of the pot allocated to train operators. 7

14 3.3 EDGE Exogenous Demand Growth Estimation 15. The EDGE database provides forecasts of the growth rates of the demand drivers identified in PDFH on a geographical basis, which can be matched with the station-to-station flows for which the forecasts of rail patronage are required. The PDFH elasticities, combined with the LENNON base year data on passenger flows on the corridor and the EDGE-based forecasts of the exogenous demand drivers, provide an initial estimate of future rail patronage. The forecasts of the variables that influence rail demand are the same as are used in the DfT s road traffic and other forecasts. 3.4 MOIRA Specification of rail services and capacity 16. The linking of demand with supply, represented through measures of the capacity and quality of the network, is a fundamental part of most transport models. This process enables policymakers to understand the impact of congestion or crowding, which, if capacity is not increased while demand grows, will inhibit the growth in demand. In addition, forecasts are often used to estimate the impact on demand, revenues and rail-user benefits of changes to the services specified in the base case, to inform decision-makers about the case for investment in capacity. 17. The supply side of the rail network is represented through the MOIRA model, which is composed of the base year and future year timetables, with any options for change set up in a separate future year timetable. The timetable includes data on train capacities. The model allocates passengers travelling between the origins and destinations identified in LENNON in both the base year and the future year flows, forecast through the PDFH elasticities in combination with the EDGE forecasts, to the trains operated in the timetable. MOIRA includes a feedback loop whereby an increase in crowding both suppresses overall demand and encourages rail users to switch to less-crowded trains despite the inconvenience of having to change their schedules. An option which increases capacity will result in passengers reverting to their preferred schedule as well as an overall increase in demand. MOIRA is also used to show the effects on demand of changes in journey time and in other attributes of the journey. These effects are expressed in the model in units of generalised journey time (GJT), with each attribute being valued in relation to what its equivalent would be if taken in terms of additional travel time. Thus a journey of 10 minutes spent in crowded conditions might have a GJT of 15 minutes (see paragraph 29 onwards). MOIRA includes a representation of passengers preferred departure times and can thus show the effect on demand of changes in the timetable. 18. There are therefore two elements of the rail forecasting framework which influence the projections of demand growth. The first relates to the identification of the key drivers of demand and the estimation of the 8

15 elasticities for those drivers. The second is the representation, through the concept of GJT, of the combined impact on demand of the qualityof-service attributes it is changes in these attributes which influence demand to an extent which is determined by the various elasticity values. 3.5 The geographical coverage of the rail forecasts 19. The rail forecasting framework composed of PDFH, EDGE and MOIRA can be operated at varying levels of spatial aggregation. Existing or potential train operators are concerned with services operated within the boundaries of their franchise, and need know much less about services operated elsewhere. The framework can be adapted to omit or amalgamate services which are of little interest to the user of the model, in order to reduce the time devoted to operating the model and to reduce the risk of error. The DfT s strategic analysis makes use of the Network Modelling Framework, a version of the rail forecasting framework which covers the entire network but amalgamates smaller stations to form a representative small station, so as to reduce the number of origin destination pairs in the model. 9

16 4. PDFH Estimation Methods 20. The size of the LENNON database and the large number of variables that influence rail travel present a number of challenges. Some influences, such as changes in fares, tend to affect either all passengers or one or more segments season ticket holders, for instance. Other influences, such as service improvements, are route-specific. The effects on demand and fare revenues of network-wide changes, such as increases in fares, will vary between routes because of different levels of crowding and a different mix of journey types and purposes, again reinforcing the need for a fairly detailed network-based model. Historical data on some of the factors that make up changes in the components of GJT (e.g. timetabled journey times) is available, while data on others, such as changes in reliability, is rarely available on a service- and route-specific level without significant additional work. LENNON data is available on a consistent basis from 1990, although 1994 data is omitted because demand during that year was affected by industrial action. Many of the values reported in PDFH are derived from the period , supplemented in the more recent editions by an extension of the dataset to 2005 for the purpose of estimating some elasticities. 21. Time series econometric methods are used to estimate the PDFH elasticity values. Deciding the extent and nature of segmentation of passenger flows requires a mixture of analysis and judgement. Segmentation by journey purpose key to many rail forecasting studies aimed at addressing the problem of peak capacity is done by ticket type, with data from the National Rail Travel Survey being used to map ticket type (anytime, off-peak or season) to journey purpose. In addition, the data is segmented into six flow types the London Travelcard area, the South East, the rest of the country to and from London (with GDP elasticities differing by direction), non-london inter-urban, non-london shortdistance (<20 miles) and airport-related flows. Each segment typically contains data relating to around 700 origin destination flows, and the data for each flow in the segment is pooled in order to reflect both overall changes in the drivers of demand over time and geographical differences, as the economies of different regions grow at different rates. 22. A number of different estimation methods have been used in the past to derive the elasticity values. In general, the more recent estimates have allowed the GDP, fares and GJT elasticities to be estimated together from the same dataset in an attempt to identify the separate effects of each of 10

17 these drivers of demand. There remains a risk of error in the attribution of some of these causes for example, while an increase in the number of services provided makes rail more attractive, and hence increases demand, such a change in rail s attractiveness is also due to a response by the train operator to the increase in passenger numbers that is attributable to the growth in GDP. In such cases, it is not always possible to distinguish between the effects of each driver of demand, and there is a risk of incorrectly attributing to one elasticity value an effect which is caused by a different driver which has changed in broadly the same direction as the effect being estimated. 23. The derivation of the elasticity values published in PDFH has been an evolutionary process. The values reflect a combination of econometric analysis of a sample of flows from the current LENNON dataset, and the judgement of analysts informed by other sources and previous LENNON-based values. There are a number of challenges that justify the modification of the values that are derived directly from the econometric analysis. Among these are: a. Broadly similar estimation techniques have resulted in significant differences in the elasticity values when the run of years included in the analysis is changed, as it has done when more recent years have been added, suggesting an unexpected element of instability, suggesting greater changes in behaviour than seem plausible. b. LENNON provides inadequate coverage of rail travel in most of the conurbations, and the attempts to augment the data are imperfect. c. Some of the aspects of GJT reliability, for example are excluded from the estimation process because historical data on changes in these influences on demand is not readily available. d. Other data for example, on changes in car journey times or in the costs and convenience of travel by air is often based on informed estimates because of the absence of good data. 11

18 4.1 GDP elasticities 24. The PDFH GDP elasticities identify the relationship between changes in GDP per capita in the nine NUTS 1 (formerly Government Office) English regions plus Scotland and Wales, and changes in rail passenger kilometres, with each flow allocated either to the region in which the trip started or to the region in which its end lay. The elasticities for business trips are estimated using data on GDP per capita at the destination of the trip, whereas changes in GDP per capita at the origin end explain the income-related elasticities for leisure trips. The weighted average GDP elasticity for all flows is above unity, as might be expected in the light of the 77% growth in rail patronage over the past 15 years, during which GDP per capita has grown by 33%. 4.2 Fares elasticities 25. Estimates of fares elasticities were derived from several separate studies which analysed flows that had experienced significant increases in fares levels. PDFH v5.0 fares elasticity values range from 0.5 in the case of commuting within the London Travelcard area to 1.2 in the case of leisure trips from the rest of the country to London. (Note the negative elasticities, which indicate that an increase in fares leads to a decrease in travel.) 26. A separate model, the Strategic Rail Authority s Strategic Fares Model, which has been updated and revised by the DfT since the abolition of the SRA in 2006, provides the basis for the guidance in PDFH on the likely switching between ticket types that occurs when the prices of different ticket types increase at different rates. 4.3 Employment and population elasticities 27. The relationship between commuting by rail and employment has been of key importance in making decisions relating to investment in rail capacity, and was the focus of the 2007 High Level Output Specification (HLOS the statutory document in which the government indicates to the Office of Rail Regulation the ORR and the rail industry the level of services that it wants to see achieved by the industry from the funding it provides during a five-year railway Control Period), and a major part of the 2012 HLOS. Employment elasticities are above unity for London and around unity for other cities. The higher-than-unity value for London might be explained in terms of the greater distance from central London at which each additional worker is likely to live as demand for housing is met by development taking place further from the centre. Thus the probability of using rail as a mode increases for each additional worker. In part this might also be accounted for by an income effect, as increasing income leads London s workers to relocate in locations further from the centre and benefit from more space, leading to a greater probability that they 12

19 will commute by rail. The employment elasticities were based largely on a judgement that the values for the growth in employment would be somewhat in excess of unity for the reasons given above. The dataset which served to inform the fare, GDP and GJT elasticities excluded data on season ticket flows and thus did not provide the data needed to make estimates of employment elasticities. For the majority of passenger flows, rail patronage is assumed to increase in line with population growth at the origin of the trip, with an elasticity of unity 3. Since commuting flows are determined by employment at the destination end of the trip, population growth has no direct effect on such flows. However, if population on a given route is forecast to increase more rapidly than the regional average, then commuting demand is predicted to increase at the same rate (i.e. with an elasticity of 1.0). No corresponding reduction is made on those routes with a below average rate of population growth. 4.4 Effects of other modes 28. Competition from other modes can have a significant effect on routespecific flows, and guidance is therefore provided in PDFH on assessing the impacts of changes in other modes on these flows. Analysis of the influences on rail demand, which includes a broad estimate of changes in car journey times, shows that an increase in road congestion is one of the factors that has contributed to the growth in rail demand, helping to explain the more rapid growth in rail trips to the increasingly congested London area. PDFH provides a series of cross elasticities for use in forecasting rail demand in response to changes in car ownership, fuel costs and journey times; and bus costs, journey times and headway. Changes in the costs of air travel and the frequency of flights influence only the longest-distance trips. Car cost and journey time elasticities are typically between 0.1 and 0.3, as are the equivalent elasticities for bus: fares and travel times. Advice is also provided on the application of diversion factors to estimate modal shift in those circumstances where rail s market share is atypical and the standard cross elasticities are therefore unreliable. Estimates of these cross elasticities have come from a number of sources. 3 That is, an expectation that a given percentage increase in population will result in exactly the same proportionate increase in patronage. 13

20 4.5 Changes in journey quality generalised journey time 29. PDFH identifies several characteristics of a rail journey which, if changed, are likely to influence demand. These attributes are expressed in broad terms of generalised cost, a unit of measurement used throughout the four-stage transport modelling process, which provides a means of putting travel time and the money costs of a trip on a common basis through use of a money value of time. The approach in PDFH differs from the conventional method, in that it omits the money element of the journey, which generally comprises the fare paid; this is both to facilitate computation and because train operators regard changes in unregulated fares as being of commercial interest, and often develop their own modelling methods in relation to this area rather than rely on PDFH. The values quoted in PDFH come from a range of stated preference studies 4 carried out at various points in time. 30. PDFH uses the concept of GJT in terms of elapsed time rather than in money values, with penalties or multipliers attached to time spent outside the rail vehicle or within a crowded vehicle. PDFH gives guidance on the weights or penalties to be put on the various attributes of a rail journey for example, season ticket holders on a journey of less than 15 miles perceive an interchange as having a value equivalent to around 10 minutes of additional travel time, whereas for a leisure traveller from King s Cross, the penalty incurred on account of having to changing trains at Newcastle, in the mile distance band, might be as much as an hour. Further penalties are applied to modify these values according to whether or not the connection is guaranteed, and the environment of the interchange station. Values are attributed to waiting and walking time for application to those schemes which affect either service frequencies or station design, and to quality of rolling stock. The effect of changes in station facilities and the overall station environment is modelled through an uplift on the initial estimate of demand for example, a shift from the absence of any information about disruptions to one in which current information is displayed might add 5% to commuter demand and 10% to business and leisure patronage. 31. The effect of changes in reliability is taken into account through putting a multiplier greater than unity on each minute by which a train is late. PDFH provides guidance on the weights on minutes travelled in crowded conditions, which vary both by level of crowding (measured in terms 4 According to the then Department for Transport, Local Government and the Regions (DTLR), Stated preference techniques rely on asking people hypothetical questions, rather like a market research interview. The aim is to see how people respond to a range of choices, and thus to establish the extent of collective willingness to pay for a particular benefit (or their willingness to accept payment in exchange for bearing a particular loss). Stated preference questionnaire-based techniques can be contrasted with revealed preference analysis which aims to deduce people s willingness to pay from observed evidence of how they behave in the face of real choices (DTLR, 2002: 7). 14

National Rail Passenger Survey Data Analysis On the Move Supporting Paper 3 Fiona Preston and Peter Jones December 2012 This report has been commissioned by: The Royal Automobile Club Foundation for Motoring

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